Travel Behavior
Behavior Data
Data Collected
Collected Using
Using GPSGPS andand PHSPHS
TravelNobuaki Ohmori1, Yasunori Muromachi2, Noboru Harata3 and Katsutoshi Ohta4
Abstract
Generally travel behavior data are collected by self-reported questionnaire surveys.
Problems with these methods include lack of reporting for short trips, poor data
quality on travel start and end times, travel times, and destination locations. The
burden on the respondent is also very large. The detailed and accurate travel data
should be needed to evaluate the effects of TDM strategies and ITS technologies on
travel behavior, but it would be difficult using traditional questionnaire surveys. This
paper aims to develop travel behavior data collecting systems using global
positioning system (GPS), personal handyphone system (PHS) and geographic
information system (GIS), and to examine the effectiveness of the data collected bythese systems and the applicability for travel behavior surveys. We conducted thesurveys of collecting travel behavior data by GPS and PHS, with travel and activity
diary over a week. We collected GPS data of vehicle trips in two local cities from 15
and 28 commuters respectively, and PHS data of person trips of 30 people in a local
city. Examples of day-to-day variability analyses of travel distances, times, speeds
and routes of trips, are shown using data collected by GPS. We examined thecharacteristics of PHS data and the differences of travel start and end times,
comparing PHS data with diary data, for elderly and non-elderly people. Also
comparison between datasets of the two age groups was done. Improvements ofthese new systems for practical use to travel surveys are summarized.
Introduction
It is the most basic and important factor to capture people’s daily travel patterns-----------------1

Doctoral student, Department of Urban Engineering, nobuaki@ut.t.u-tokyo.ac.jp
Associate professor, Engineering Research Institute3
Professor, Graduate School of Frontier Sciences
4
Professor, Department of Urban Engineering
The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan
2

1

Ohmori, Muromachi, Harata and Ohtaaccurately in urban transportation planning. The traditional travel surveys such as
person trip surveys are conducted as self-reported questionnaire surveys. Problems
with these methods include lack of reporting for short trips, poor data quality on
travel start and end times, travel times, and destination locations. Activity diarysurveys which request respondents to record all activities in a day are noteworthy inpoint of ability of correcting lacked trips compared with travel diary surveys. But theburden of respondents in activity diary surveys is larger than in travel diary surveys.
Moreover, it is difficult to record routes of trips because of respondents’ burden andthe survey cost. The factor of deciding the accuracy of analyses and forecasting
depends on the quality of input data, collecting more detailed and accurate travel
data is essential to evaluate the effects of TDM strategies and ITS technologies.
There are many possibilities for global positioning system (GPS) and personal
handyphone system (PHS) greatly contributing to travel behavior surveys. It is
possible to collect data of dates and times, and positions (longitude/latitude)
automatically using GPS or PHS (GPS can collect data of speeds), so we can collectthe detailed and accurate data of start and end times, origin and destination locations,routes and speeds of trips if people carry the equipment when they travel. Data oflocations and speeds collected at 1-second intervals by GPS would give effective
information when analyzing the changes of start times, routes and travel times byintroduction of TDM and ITS, travel behavior of drivers searching parking places byparking information systems, tourist travel and calculating volume of automobile
emissions such as CO2 and NOx. PHS could collect detailed spacio-temporal
behavioral data, since it has many advantages that it is portable for people in theirpocket or bag, and it can also collect location data in the buildings and subway
stations. Both of these alleviate the burden of respondents by collecting
automatically spacio-temporal travel behavior data, so they would make it easier to
conduct surveys of multiple days or to obtain additional data (Jones, 1997).
Recently several studies that examined the characteristics of travel data using GPS,
PHS and GIS, have been reported. Murakami and Wagner (1999) compared
computer-assisted self-interviewing using GPS with retrospective trip reporting
using telephone interviews. They showed the differences of travel distances andtravel times comparing both data, and gave examples of the differences of speeds byclasses of roads. Quiroga and Bullock (1998) and Schütte, et al. (1999) used GPS totravel time studies. PHS location tracking systems have been available since 1998 in
Japan, the characteristics of data and the applicability to travel behavior surveys
have been analyzed (Hato, et al., 1999, Takahashi, et al., 1999, Terabe, 1999).
GPS data collecting and analyzing system
GPS is the global navigation satellite system employed by U. S. Department of
Defense. The methods of navigation are classified into three types that are “SPS
(Standard Positioning Service)”, “DGPS (Differential GPS)” and “carrier phase
DGPS” (see Table 1.). “Carrier phase DGPS” is very complicated and expensive, so“SPS” or “DGPS” are thought to be proper for collecting OD data and routes of2

Ohmori, Muromachi, Harata and Ohta

person or vehicle trips.
GPS data may be missed due to satellite “loss of fix”, urban canyons, dense tree
cover. There is little possibility to receive signals from satellite inside the wagon oftrain and bus not to mention in the buildings, so it is available to collect travel databy only mode of walk, bicycle and vehicle.
Devices for collecting and analyzing spacio-temporal data of trips using GPS are(1) GPS antenna and receiver, (2) data collector and (3) GIS software. Two kinds ofmethods are possible using ready-made goods.
System 1: carrying the hardware combined GPS antenna, receiver and datacollector, and collecting data in it.
System 2: carrying GPS antenna and receiver connected portable computer, andcollecting data in the computer.
In both of two systems, after collecting data we transfer data to main computer andanalyze on GIS software. System 1 has many problems as battery, operation,
capacity of memory, and in terms of portability since the device must be kept so thatit can receive signals from a clear sky, therefore it is not suited to collecting multiple
days trip data. System 2 is exclusive for collecting vehicle trip data but we tried todevelop the system that can collect trip data of multiple days by simple operation.
Supplying power of GPS and portable computer from vehicle cigarette lighter andusing the batch file enhancer software for Windows95 (WinbatchEh), GPS data canbe collected by pushing one or two button when people drive a car. Therefore it
continually collects GPS data of consecutive days for a week, month or year, within
capacity of hard disk of a portable computer.
System 1 use “GeoExplorerⅡ” as GPS receiver and “Pathfinder Office” as data
handling software by Trimble corporation. In System 2, small and low-priced GPS,for example, Garmin “GPS35” and Sony “IPS-5000”, is attached through the serial
port of the portable computer. We used MapInfo GIS software for displaying andanalyzing GPS data.
PHS data collecting and analyzing system
PHS has location tracking systems other than primary function of cellular phone.
PHS decide its location using information of plural base stations which PHS access,we can get data of dates and times, positions (longitude/latitude) and the name ofbuildings where PHS base station is placed. The area, where location data can becollected, is limited to where PHS service is available, but we can get location dataof a person even if PHS is in his pocket or bag, or he is in buildings or subway
stations. The weak point is that data cannot be collected when he moves with highspeeds (more than 50-80 km/h) and the logging intervals are longer than GPS (15seconds at minimum).
Devices for collecting and analyzing spacio-temporal data of trips using PHS are(1) PHS receiver for location tracking, (2) host computer and PHS (or ISDN
telephone wires) for connecting to the location information center and (3) GIS
software. In this study, we used PHS location tracking and data collecting systems
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Ohmori, Muromachi, Harata and Ohta

Table 1. Comparison of data characteristics and applicable fields of travel datacollected by GPS and PHS
Information Horizontal
neededaccuracy
more
than
3
SPS
-100m
or 4 satellites
Real-time +base data
5-10m
G DGPS
Post
P
5-10m
processed +base dataS
several mm
Carrier phase
+base
data
DGPS
several cm
P
HS

Tracking
intervals

Travel Modes

1 sec.

1 sec.

Walk, Bicycle,
VehicleVehicleWalk, Bicycle,
Vehicle1 sec.

Vehicle1 sec.

NTT Docomo

1 or 2 PHSbase stations

100-200m

5 min.

Toshiba,
Video research,
Locus

plural (-10)
PHS basestations

50-80m15 sec.

modes ofspeed less than
50km/hmodes ofspeed less than
100km/hApplicable
fieldsOD, route,speed
OD, route,speed
OD, route,speed
OD, route,speed,
position ofroad
OD,
activity
location
OD,
activity
location,routeby NTT Docomo and Fujitsu SSL. We used MapInfo for displaying and analyzing
PHS data on GIS. Table 1 summarizes data characteristics and applicable fields oftravel data collected by GPS and PHS.
Vehicle trip surveys using GPS data collectiing system
Probability measures for successful reception of GPS signal and creation of trip
data as well as accuracy of locations and speeds are known in previous studies (Zito,
et al., 1995, Ohmori, et al., 1998). In this study, we collected GPS data and travel
diary data of vehicle trips and investigated travel behavior using these detailed andaccurate travel data.
We conducted two surveys for 15 drivers who commute to Tochigi prefecture
office for civil engineering works in January, 1999, for a week (survey 1), and for 28
drivers who commute to Toyota municipal office in March, 1999, for a week (survey
2). Respondents were also asked to report travel start and end times, trip purposes
and the name of the destination buildings. Table 2 shows travel start times, travel
distances and travel times of commuting trips and all trips of a sample from survey 1.
He traveled in the same routes but we can know there is the variability of 8 minutes
among 5 days (Monday to Friday). Figure 1 shows point speeds of two days
commuting trips, one of which is of Thursday with the minimum travel time andanother is of Tuesday with the maximum travel time. On Thursday, this road might
be congested at the point shown in the figure, so we can identify that it took longer
travel time than on Tuesday.
Using data from survey 1 only, Figure 2 shows the amount of variation ofcommuting travel times traveled in a week by all samples using the same route andthe same start time (with a maximum of 10-minutes variation). The variation of
travel times ranges from 3 to 11 minutes, so the conditions of road congestions are4

Ohmori, Muromachi, Harata and Ohta

Table 2.

Trip start times, trip distances and travel times by GPS data

Start time
1/26 Tue.
1/27 Wed.
1/28 Thu.
1/29 Fri.
1/30 Sat.
1/31 Sun.
2/1 Mon.

7:52:19
7:51:02
7:53:38
7:52:08――――
7:51:27

Commuting trips
Trip distance
Travel time(km)
(min.)
14.02
32
13.93
30
13.94
24
13.87
28
――
――
――
――
14.47
32

speed (km/h)
4020-400-20

0End time
8:18

All trips
Trip distance Travel time(km)
(min.)
27.67
48
27.61
49
27.56
41
50.44
81
26.38
40
55.63
102
32.83
65
End time
8:24

speed (km/h)
4020-40
0-20

2km

0

2km

Low speed

Start time
7:54

Start time
7:52

1/28 Thu.

1/26 Tue.

Figure 1. Speeds of two days commuting trips

Travel time (min.)

80
70
60
50
40
30
20
10
0
0

10

20

30

40

50

60

70

80

Average travel time (min.)Figure 2. The amount of variation of commuting travel times

5

Ohmori, Muromachi, Harata and Ohta

not the same everyday. Such data about travel times, routes and speeds are needed
for making detailed and accurate before-and-after analyses estimating the effects of
TDM and ITS. Therefore GPS is thought to be a very promising tool.
Person Trip Survey Using PHS Data Collecting System
The survey was conducted in Akita city, in February 1999, 30 people over age 15(20 are over 65 years and 10 are under 65 years) in 13 households. Respondents
were asked to record travel diary and activity diary over a seven-day period, and tocarry PHS when they go out. We collected PHS data at 5-minutes intervals from AM
5:00 to PM 11:00 during the survey period. Percentages of PHS data successfullycollected range from 33% to 97% dependent on locations where they live and visited(Table 3.). We compared the number of trips, which are specified from travel diary,
activity diary and PHS data (Table 3.). Trips of 5 elderly and 1 non-elderly samples,
which were lacking in travel diary data, were retrieved from activity diary data (Trip(B)). In addition, trips of 12 elderly and 7 non-elderly samples, which were notrecorded in travel diary and activity diary data, were also retrieved from PHS data
(Trip (C)). But all trips cannot be captured from only PHS data, because we can not
specify trips if he visited at the places very near his house or stayed at out-of-homeactivity locations for very short time, add to the reason of low percentages of PHS
data (see Trip (D)). Percentage of not-reported trips in diary data is higher for
elderly than non-elderly samples. Comparing diary data and PHS data, we foundmany cases, which he recorded that he traveled but PHS location data had been in
his home at that time, maybe he would have forgotten to carry PHS when he went
out. We defined such trips as “Trip (E)” and percentage of Trip (E) is also higher inelderly than non-elderly samples.
Figure 3 shows the difference of trip start and end times between diary data andPHS data. More than 60% of commuting and education trips of non-elderly samples
show differences of less than 5 minutes. The difference of other purpose trips ofnon-elderly is larger, and that of elderly is much larger, 10% of them are more than
45 minutes so they would mistake trip start and end times when they reported.
Recommended improvements of the systems developed in this study
We summarize future direction of improvements of these systems for practical use
to travel behavior surveys.
For GPS data collecting system:- Reduction of starting time at the beginning of collecting GPS data (nowrespondents have to wait for about 30 seconds before they travel).
- Smaller devices for data collector.
- Stability of data collection systems (some trips were missed due to equipment
malfunction).
For PHS data collection system:- To prevent people from forgetting to carry PHS when they go out.
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Ohmori, Muromachi, Harata and Ohta

Table 3. Percentage of PHS data collected and comparison of the number of trips
between travel diary, activity diary and PHS data
HH ID % of PHS
All trips
Trip (A) Trip (B) Trip (C) Trip (D) Trip (E)NO. NO.
data(A)+(B)+(C)
1
1
96.8%
21
7
10
4
4
17
2
2
97.7%
9
7
0
2
7
2
3
3
76.7%
18
18
0
0
13
0
4
4
89.7%
18
18
0
0
12
6
5
5
81.3%
20
20
0
0
8
10
5
6
62.3%
27
23
2
2
6
6
6
7
73.7%
22
20
0
2
18
2
6
8
81.3%
0
0
0
0
0
0
65
7
9
72.5%
41
39
0
2
34
0
years
7
10
78.1%
17
17
0
0
16
0
and
8
11
94.5%
11
9
0
2
6
0
over
8
12
93.2%
19
19
0
0
12
0
9
13
91.6%
27
18
0
9
19
5
9
14
90.2%
4
3
1
0
3
1
10
19
96.5%
9
6
2
1
7
2
11
23
64.9%
18
15
1
2
6
0
12
24
67.9%
10
10
0
0
5
4
12
25
84.0%
4
2
0
2
2
0
12
26
59.9%
32
31
0
1
18
9
13
30
69.0%
16
15
0
1
5
11
Percentage of each trip
100.0%
86.6%
4.7%
8.7%
58.6%
21.9%
9
15
34.1%
17
15
0
2
11
0
9
16
70.7%
26
26
0
0
12
7
10
17
65.5%
16
13
0
3
13
3
10
18
53.6%
23
22
0
1
18
4
under
11
20
68.8%
22
21
0
1
14
8
65
11
21
45.9%
42
19
20
3
5
17
years
11
22
80.9%
15
15
0
0
10
3
13
27
97.0%
6
4
0
2
2
4
13
28
34.7%
58
58
0
0
30
6
13
29
33.4%
23
22
0
1
17
0
Percentage of each trip
100.0%
86.7%
8.1%
5.2%
53.2%
21.0%

Trip (A): Number of trips from travel diary survey
Trip (B): Number of trips retrieved from activity diary survey
Trip (C): Number of trips retrieved from PHS data
Trip (D): Number of trips specified from only PHS data
Trip (E): Number of trips when respondents forgot to carry PHS
65 years undercommuting andeducation trips±less than 5min.
±5-15min.

65 years underother purpose
trips±15-25min.
±25-35min.
±35-45min.

65+ years
all purpose
trips±45min. over

0%

20%

40%

60%

80%

100%

Figure 3. Differences of trip start and end times between diary data and PHS data
7

Ohmori, Muromachi, Harata and Ohta-

Development of methods or computer algorithms for dividing PHS data into
trips and activities (PHS data show variable locations even if it stays at one
location).

Conclusion
In this study, we developed the travel data collecting and analyzing systems using
GPS, PHS and GIS, and conducted travel behavior surveys over a seven-days period.
Simple examples of day-to-day variability analyses about trip start times, travel
times and speeds are shown using GPS data of vehicle trips. From PHS data, wefound not-reported trips in travel and activity diary data, and error of trip start andend times recorded in self-reported questionnaire survey, comparing elderly andnon-elderly samples. Lastly, future direction of improvements of these systems forpractical use to travel behavior surveys was summarized.
There are relationships of trade off between accuracy, quantity and survey cost indata collected using these new systems, so we have to investigate appropriate system
for travel data needs of analyses. Fast development in the fields of information andcommunication technology would make cost of equipments lower. The methods ofhandling and analyzing a vast amount of spacio-temporal data should be examined.
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Ohmori, Muromachi, Harata and Ohta